# --------------------------------------------------------
# SimMIM
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# Modified by Zhenda Xie
# --------------------------------------------------------

import os
import time
import argparse
import datetime
import numpy as np

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist

from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
# ----------npu modified start----------
use_NPU = True
try:
    if torch.__version__ >= "1.8":
        import torch_npu
    else:
        import torch.npu
except Exception as e:
    print(e)
    print('Use GPU to run code.')
    use_NPU = False
print("use_NPU:", use_NPU)
# ---------npu modified end-------------

from config import get_config
from models import build_model
from data import build_loader
from lr_scheduler import build_scheduler
from optimizer import build_optimizer
from logger import create_logger
from utils import load_checkpoint, load_pretrained, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor

try:
    # noinspection PyUnresolvedReferences
    from apex import amp
except ImportError:
    amp = None


def parse_option():
    parser = argparse.ArgumentParser('Swin Transformer training and evaluation script', add_help=False)
    parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', )
    parser.add_argument(
        "--opts",
        help="Modify config options by adding 'KEY VALUE' pairs. ",
        default=None,
        nargs='+',
    )

    # easy config modification
    parser.add_argument('--batch-size', type=int, help="batch size for single GPU")
    parser.add_argument('--data-path', type=str, help='path to dataset')
    parser.add_argument('--pretrained', type=str, help='path to pre-trained model')
    parser.add_argument('--resume', help='resume from checkpoint')
    parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps")
    parser.add_argument('--use-checkpoint', action='store_true',
                        help="whether to use gradient checkpointing to save memory")
    parser.add_argument('--amp-opt-level', type=str, default='O1', choices=['O0', 'O1', 'O2'],
                        help='mixed precision opt level, if O0, no amp is used')
    parser.add_argument('--output', default='output', type=str, metavar='PATH',
                        help='root of output folder, the full path is <output>/<model_name>/<tag> (default: output)')
    parser.add_argument('--tag', help='tag of experiment')
    parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
    parser.add_argument('--throughput', action='store_true', help='Test throughput only')

    # distributed training
    parser.add_argument("--local_rank", type=int, required=True, help='local rank for DistributedDataParallel')

    args = parser.parse_args()

    config = get_config(args)

    return args, config


def main(config):
    dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(config, logger, is_pretrain=False)

    logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
    model = build_model(config, is_pretrain=False)
    # ----------npu modified start----------
    if use_NPU:
        model.npu()
    else:
        model.cuda()
    # ---------npu modified end-------------
    logger.info(str(model))

    optimizer = build_optimizer(config, model, logger, is_pretrain=False)
    if config.AMP_OPT_LEVEL != "O0":
        model, optimizer = amp.initialize(model, optimizer, opt_level=config.AMP_OPT_LEVEL)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
    model_without_ddp = model.module

    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    logger.info(f"number of params: {n_parameters}")
    if hasattr(model_without_ddp, 'flops'):
        flops = model_without_ddp.flops()
        logger.info(f"number of GFLOPs: {flops / 1e9}")

    lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))

    if config.AUG.MIXUP > 0.:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif config.MODEL.LABEL_SMOOTHING > 0.:
        criterion = LabelSmoothingCrossEntropy(smoothing=config.MODEL.LABEL_SMOOTHING)
    else:
        criterion = torch.nn.CrossEntropyLoss()

    max_accuracy = 0.0

    if config.TRAIN.AUTO_RESUME:
        resume_file = auto_resume_helper(config.OUTPUT, logger)
        if resume_file:
            if config.MODEL.RESUME:
                logger.warning(f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}")
            config.defrost()
            config.MODEL.RESUME = resume_file
            config.freeze()
            logger.info(f'auto resuming from {resume_file}')
        else:
            logger.info(f'no checkpoint found in {config.OUTPUT}, ignoring auto resume')

    if config.MODEL.RESUME:
        max_accuracy = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, logger)
        acc1, acc5, loss = validate(config, data_loader_val, model)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        if config.EVAL_MODE:
            return
    elif config.PRETRAINED:
        load_pretrained(config, model_without_ddp, logger)

    if config.THROUGHPUT_MODE:
        throughput(data_loader_val, model, logger)
        return

    logger.info("Start training")
    start_time = time.time()
    for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
        data_loader_train.sampler.set_epoch(epoch)

        train_one_epoch(config, model, criterion, data_loader_train, optimizer, epoch, mixup_fn, lr_scheduler)
        if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0 or epoch == (config.TRAIN.EPOCHS - 1)):
            save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger)

        acc1, acc5, loss = validate(config, data_loader_val, model)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        max_accuracy = max(max_accuracy, acc1)
        logger.info(f'Max accuracy: {max_accuracy:.2f}%')

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    logger.info('Training time {}'.format(total_time_str))


def train_one_epoch(config, model, criterion, data_loader, optimizer, epoch, mixup_fn, lr_scheduler):
    model.train()
    optimizer.zero_grad()
    
    logger.info(f'Current learning rate for different parameter groups: {[it["lr"] for it in optimizer.param_groups]}')

    num_steps = len(data_loader)
    batch_time = AverageMeter()
    loss_meter = AverageMeter()
    norm_meter = AverageMeter()

    start = time.time()
    end = time.time()
    for idx, (samples, targets) in enumerate(data_loader):
        # ----------npu modified start----------
        if use_NPU:
            samples = samples.npu(non_blocking=True)
            targets = targets.npu(non_blocking=True)
        else:
            samples = samples.cuda(non_blocking=True)
            targets = targets.cuda(non_blocking=True)
        # ---------npu modified end-------------

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)

        outputs = model(samples)

        if config.TRAIN.ACCUMULATION_STEPS > 1:
            loss = criterion(outputs, targets)
            loss = loss / config.TRAIN.ACCUMULATION_STEPS
            if config.AMP_OPT_LEVEL != "O0":
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(amp.master_params(optimizer))
            else:
                loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(model.parameters())
            if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0:
                optimizer.step()
                optimizer.zero_grad()
                lr_scheduler.step_update(epoch * num_steps + idx)
        else:
            loss = criterion(outputs, targets)
            optimizer.zero_grad()
            if config.AMP_OPT_LEVEL != "O0":
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(amp.master_params(optimizer))
            else:
                loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(model.parameters())
            optimizer.step()
            lr_scheduler.step_update(epoch * num_steps + idx)

        # ----------npu modified start----------
        if use_NPU:
            torch.npu.synchronize()
        else:
            torch.cuda.synchronize()
        # ---------npu modified end-------------

        loss_meter.update(loss.item(), targets.size(0))
        norm_meter.update(grad_norm)
        batch_time.update(time.time() - end)
        end = time.time()

        if idx % config.PRINT_FREQ == 0:
            lr = optimizer.param_groups[-1]['lr']
            # ----------npu modified start----------
            if use_NPU:
                memory_used = torch.npu.max_memory_allocated() / (1024.0 * 1024.0)
            else:
                memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            # ---------npu modified end-------------
            etas = batch_time.avg * (num_steps - idx)
            logger.info(
                f'Train: [{epoch}/{config.TRAIN.EPOCHS}][{idx}/{num_steps}]\t'
                f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t'
                f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
                f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
                f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
                f'mem {memory_used:.0f}MB')
    epoch_time = time.time() - start
    logger.info(f"EPOCH {epoch} training takes {datetime.timedelta(seconds=int(epoch_time))}")


@torch.no_grad()
def validate(config, data_loader, model):
    criterion = torch.nn.CrossEntropyLoss()
    model.eval()

    batch_time = AverageMeter()
    loss_meter = AverageMeter()
    acc1_meter = AverageMeter()
    acc5_meter = AverageMeter()

    end = time.time()
    for idx, (images, target) in enumerate(data_loader):
        # ----------npu modified start----------
        if use_NPU:
            images = images.npu(non_blocking=True)
            target = target.npu(non_blocking=True)
        else:
            images = images.cuda(non_blocking=True)
            target = target.cuda(non_blocking=True)
        # ---------npu modified end-------------

        # compute output
        output = model(images)

        # measure accuracy and record loss
        loss = criterion(output, target)
        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        acc1 = reduce_tensor(acc1)
        acc5 = reduce_tensor(acc5)
        loss = reduce_tensor(loss)

        loss_meter.update(loss.item(), target.size(0))
        acc1_meter.update(acc1.item(), target.size(0))
        acc5_meter.update(acc5.item(), target.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if idx % config.PRINT_FREQ == 0:
            # ----------npu modified start----------
            if use_NPU:
                memory_used = torch.npu.max_memory_allocated() / (1024.0 * 1024.0)
            else:
                memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            # ---------npu modified end-------------
            logger.info(
                f'Test: [{idx}/{len(data_loader)}]\t'
                f'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                f'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
                f'Acc@1 {acc1_meter.val:.3f} ({acc1_meter.avg:.3f})\t'
                f'Acc@5 {acc5_meter.val:.3f} ({acc5_meter.avg:.3f})\t'
                f'Mem {memory_used:.0f}MB')
    logger.info(f' * Acc@1 {acc1_meter.avg:.3f} Acc@5 {acc5_meter.avg:.3f}')
    return acc1_meter.avg, acc5_meter.avg, loss_meter.avg


@torch.no_grad()
def throughput(data_loader, model, logger):
    model.eval()

    for idx, (images, _) in enumerate(data_loader):
        # ----------npu modified start----------
        if use_NPU:
            images = images.npu(non_blocking=True)
        else:
            images = images.cuda(non_blocking=True)
        # ---------npu modified end-------------
        batch_size = images.shape[0]
        for i in range(50):
            model(images)
        # ----------npu modified start----------
        if use_NPU:
            torch.npu.synchronize()
        else:
            torch.cuda.synchronize()
        # ---------npu modified end-------------
        logger.info(f"throughput averaged with 30 times")
        tic1 = time.time()
        for i in range(30):
            model(images)
        # ----------npu modified start----------
        if use_NPU:
            torch.npu.synchronize()
        else:
            torch.cuda.synchronize()
        # ---------npu modified end-------------
        tic2 = time.time()
        logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}")
        return


if __name__ == '__main__':
    _, config = parse_option()

    if config.AMP_OPT_LEVEL != "O0":
        assert amp is not None, "amp not installed!"

    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ['WORLD_SIZE'])
        print(f"RANK and WORLD_SIZE in environ: {rank}/{world_size}")
    else:
        rank = -1
        world_size = -1
    # ----------npu modified start----------
    if use_NPU:
        torch.npu.set_device(config.LOCAL_RANK)
        torch.distributed.init_process_group(backend='hccl', world_size=world_size, rank=rank)
    else:
        torch.cuda.set_device(config.LOCAL_RANK)
        torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
    # ---------npu modified end-------------
    torch.distributed.barrier()

    seed = config.SEED + dist.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    cudnn.benchmark = True

    # linear scale the learning rate according to total batch size, may not be optimal
    linear_scaled_lr = config.TRAIN.BASE_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    linear_scaled_warmup_lr = config.TRAIN.WARMUP_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    linear_scaled_min_lr = config.TRAIN.MIN_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    # gradient accumulation also need to scale the learning rate
    if config.TRAIN.ACCUMULATION_STEPS > 1:
        linear_scaled_lr = linear_scaled_lr * config.TRAIN.ACCUMULATION_STEPS
        linear_scaled_warmup_lr = linear_scaled_warmup_lr * config.TRAIN.ACCUMULATION_STEPS
        linear_scaled_min_lr = linear_scaled_min_lr * config.TRAIN.ACCUMULATION_STEPS
    config.defrost()
    config.TRAIN.BASE_LR = linear_scaled_lr
    config.TRAIN.WARMUP_LR = linear_scaled_warmup_lr
    config.TRAIN.MIN_LR = linear_scaled_min_lr
    config.freeze()

    os.makedirs(config.OUTPUT, exist_ok=True)
    logger = create_logger(output_dir=config.OUTPUT, dist_rank=dist.get_rank(), name=f"{config.MODEL.NAME}")

    if dist.get_rank() == 0:
        path = os.path.join(config.OUTPUT, "config.json")
        with open(path, "w") as f:
            f.write(config.dump())
        logger.info(f"Full config saved to {path}")

    # print config
    logger.info(config.dump())

    main(config)
